Neuroscience

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"Neuroscience: A Mathematical Primer" by Alwyn Scott (Springer, 2002). Submitted by: M.D. Goldfinger, Ph.D., Anatomy & Physiology Department., Wright State University, Dayton, Ohio, USA; [email protected]

The study of the brain - Neuroscience - is one of contemporary science's most challenging realms. It is a complex field with dimensions of inquiry available to a variety of scientific and medical fields of study. For those readers interested in neuroscience and who have a background in physical science, engineering, or mathematics, Alwyn Scott's book provides an ideal port of entry. Dr. Scott's book is a systematic `bottom-up' approach to the workings of the nervous system from the cellular to network perspectives. Each of the twelve chapters and six appendices provides a quantitatively-oriented view of the salient physical features of neurons.

Dr. Scott is a highly-accomplished scholar of non-linearities in physical systems. His scientific career includes academic and research positions in electrical engineering, computer science, and mathematics at University of Wisconsin, Los Alamos National Labs, University of Arizona, and others. In addition to these accomplishments, the author has published fundamental studies on information propagation in nerve fibers. In his book, Dr. Scott addresses neuronal function from these unique and remarkably eclectic disciplinary perspectives. He writes in terms of physical principles, using the language of mathematics and the insights of engineering. This approach is - alas - not typical in neuroscience, which is often descriptive, avoiding the underlying physical complexities.

The first nine chapters cover much of the salient biophysics of neurons. The topics include the physical properties of the neuron and its material components, the mechanisms whereby it captures and harnesses local energy gradients for the generation of electrical currencies, and how such signals are organized as information-containing entities which are modified and disseminated through the substance of the cell, over long distances and through much geometrical complexity. In his presentation, the author covers both well-known as well as little-known or neglected literature and concepts, an invaluable scholarly service which recaptures much significant progress ignored or forgotten elsewhere (the book's bibliographies alone are worth the price of admission). The final three chapters provide an introduction to the loftier issues of neuronal assemblies. All chapters include a recapitulation, which are invaluable for readers new to the subject.

The author does not simply provide a review of the many topics in quantitative neuroscience. Rather, Dr. Scott gives the reader a totally original and carefully-constructed step-by-step development of each subject. In those fields most familiar to this reviewer, the author's unique insights and lucid explanations are invaluable, authentically thought-provoking, and highly influential in my current research.

For neuroscience students and researchers coming from traditional biology backgrounds, this book provides an important opportunity to share in the unique perspective that physical science brings to neuroscience, aspects which many of us never experienced and were untrained to even imagine. For such readers, this book also will help to expand your knowledge of mathematical expression as applied to a familiar realm. Younger neuroscientists in particular who feel unsatisfied with the traditional descriptive approach can explore a new world of insight through the eyes of a brilliant analyst and a caring teacher.

In summary, Dr. Scott's book is both a highly-informative reference as well as a superb tutorial on the biophysics of neurons. Its content will be very useful for both new students of neuroscience as well for advanced students and researchers in this field. For readers from backgrounds in physics, engineering, and mathematics, this book will bring you comfortably into the neuroscience domain. For instructors seeking a textbook in theoretical neuroscience, computational neuroscience, or mathematical neurobiology courses, this book will be useful as a superb primary text or source for extra readings.

In short, "Neuroscience: A Mathematical Primer" by Alwyn Scott is highly recommended. Dr. Scott has made an invaluable contribution to the neuroscience literature...it is truly an instant classic!

Author(s): Alwyn Scott
Edition: 1
Publisher: Springer
Year: 2002

Language: English
Pages: 373

Preface......Page 8
Acknowledgments......Page 12
Contents......Page 14
List of Figures......Page 20
1.1 Dynamics of a Nerve Impulse......Page 22
1.2 The Structure of a Nerve Cell......Page 29
1.3 Organization of the Brain......Page 32
References......Page 40
2.1 A Generic Neuron......Page 46
2.2.1 Newtonian Dynamics of Molecules......Page 49
2.2.2 Nonlinear Diffusion of a Nerve Impulse......Page 52
2.2.3 A Qualitative Comparison......Page 54
2.3.1 Chemical Synapses......Page 56
2.3.2 Gap Junctions......Page 60
2.4.1 The McCulloch–Pitts (M–P) Neuron......Page 62
2.4.2 The Multiplex Neuron......Page 64
2.4.3 Real Neurons?......Page 65
2.5 Recapitulation......Page 66
References......Page 67
3 Nerve Membranes......Page 70
3.1 Lipid Bilayers......Page 71
3.2 Membrane Capacitance......Page 74
3.3.1 Conduction Current......Page 77
3.3.2 Diffusion Current......Page 78
3.3.3 Einstein’s Relation......Page 79
3.4 A Membrane Model......Page 81
3.5 Resting Potential and the Sodium–Potassium Pump......Page 84
References......Page 86
4.1 Space and Voltage Clamping......Page 88
4.2 Ionic Currents Through a Patch of Squid Membrane......Page 91
4.3 Space-Clamped Action Potentials......Page 95
4.4 The “Cable Equation”......Page 98
4.5 Traveling-Wave Solutions of the Hodgkin–Huxley Equations......Page 100
4.5.1 Phase-Space Analysis......Page 101
4.5.2 Numerical Results......Page 103
4.6 Degradation of a Squid Nerve Impulse......Page 105
4.7 Refractory and Enhancement Zones......Page 108
References......Page 112
5.1 Leading-Edge Approximation for the H–H Impulse......Page 116
5.2 Traveling-Wave Solutions for Leading-Edge Models......Page 119
5.2.1 Phase-Plane Analysis......Page 120
5.2.2 Analytic Results......Page 123
5.3 The Threshold Impulse......Page 127
5.4 Stability of Simple Traveling Waves......Page 129
5.5 Leading-Edge Charge and Impulse Ignition......Page 130
5.6 Recapitulation......Page 132
References......Page 133
6.1 The Markin–Chizmadzhev (M–C) Model......Page 136
6.2 FitzHugh–Nagumo (F–N) Models......Page 143
6.3 Phase-Space Analysis of an F–N Model......Page 145
6.4 Power Balance for Traveling Waves......Page 148
6.5.1 Rapid and Relaxing Regimes......Page 151
6.5.2 Stability......Page 153
6.6 Recapitulation......Page 157
References......Page 158
7 Myelinated Nerves......Page 160
7.1 An Electric Circuit Model......Page 161
7.2 Impulse Speed and Failure......Page 165
7.2.1 Continuum Limit......Page 166
7.2.2 Saltatory Limit......Page 167
7.2.3 Numerical Results......Page 168
7.3.1 Frog Motor Nerves......Page 169
7.3.2 Other Vertebrates......Page 172
7.3.3 An Evolutionary Perspective......Page 174
7.4 Recapitulation......Page 179
References......Page 180
8.1 Empirical Evidence......Page 186
8.2 M–C Analysis of Ephaptic Coupling......Page 188
8.3.1 Sketch of the Perturbation Theory......Page 190
8.3.2 A Qualitative Analysis......Page 192
8.4 Ephaptic Coupling in an F–N Model......Page 195
8.5.1 A Numerical Model for Myelinated Interactions......Page 198
8.5.2 Neurological Implications......Page 202
8.6 Recapitulation......Page 203
References......Page 204
9 Neural Modeling......Page 208
9.1.1 Passive Dendrites......Page 209
9.1.2 Decremental Conduction......Page 215
9.1.3 Rall’s Equivalent Cylinder......Page 216
9.2.1 Tapered Fibers......Page 220
9.2.2 Varicosities and Impulse Blockage......Page 222
9.2.3 Branching Regions......Page 225
9.3 Information Processing in Dendrites......Page 227
9.3.1 Dendritic Logic......Page 228
9.3.2 Multiplicative Nonlinearities......Page 234
9.4 Axonal Information Processing?......Page 238
9.5 Numerical Models......Page 241
9.6 Some Outstanding Research Problems......Page 243
9.7 Recapitulation......Page 245
References......Page 246
10 Constructive Brain Theories......Page 254
10.1 Nets Without Circles......Page 255
10.1.1 McCulloch–Pitts (M–P) Networks......Page 256
10.1.2 Learning Networks......Page 258
10.2.1 General Boolean Networks......Page 262
10.2.2 Attractor Neural Networks......Page 265
10.3 Field Theories for the Neocortex......Page 269
References......Page 273
11 Neuronal Assemblies......Page 278
11.1 Birth of the Cell-Assembly Theory......Page 279
11.2 Early Evidence for Cell Assemblies......Page 282
11.3.1 Ignition of an Assembly......Page 287
11.3.2 Inhibition among Assemblies......Page 291
11.4 How Many Assemblies Can There Be?......Page 295
11.5 Cell Assemblies and Associative Networks......Page 298
11.6 More Realistic Assembly Models......Page 299
11.7 Recent Evidence for Cell Assemblies......Page 303
11.8 Recapitulation......Page 308
References......Page 309
12.1 The Biological Hierarchy......Page 314
12.1.1 Biological Reductionism......Page 315
12.1.2 Objections to Reductionism......Page 317
12.2 The Cognitive Hierarchy......Page 326
12.3 Some Outstanding Questions......Page 330
References......Page 332
A Conservation Laws and Conservative Systems......Page 336
References......Page 339
B Hodgkin–Huxley Dynamics......Page 340
References......Page 341
C Fredholm’s Theorem......Page 342
References......Page 343
D Stability of Axonal Impulses......Page 344
References......Page 351
E Perturbation Theory for the F–N Impulse......Page 352
References......Page 354
F.1 Leading-Edge Interactions......Page 356
F.2 The FitzHugh–Nagumo System......Page 358
References......Page 361
A......Page 362
C......Page 363
E......Page 365
F......Page 366
I......Page 367
L......Page 368
M......Page 369
P......Page 370
R......Page 371
S......Page 372
Z......Page 373